Systems and methods for automated association of product information with electronic shelf labels

ABSTRACT

Systems and methods that employ an autonomous robotic vehicle (ARV) alone or in combination with a remote computing device during the installation of electronic shelf labels (ESLs) in a facility are discussed. The ARV may detect pre-existing product information from paper labels located on modular units prior to their removal and then detect the location of electronic shelf labels (ESLs) after installation. Pre-existing product information gleaned from the paper labels is associated with the corresponding ESLs. The ARV may also determine compliance or non-compliance of modular units to which an ESL is affixed with a planogram of the facility.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation application of U.S. application Ser.No. 16/935,688, filed Jul. 22, 2020, which claims priority from U.S.Provisional Application No. 62/878,162, filed Jul. 24, 2019, the entirecontents of the above applications being incorporated herein byreference in their entirety.

BACKGROUND

Electronic shelf labels (ESLs) are gaining greater acceptance in theretail environment. Unlike standard paper shelf labels, informationdisplayed on ESLs can be automatically updated from a central controlserver.

BRIEF DESCRIPTION OF DRAWINGS

Illustrative embodiments are shown by way of example in the accompanyingdrawings and should not be considered as a limitation of the presentdisclosure.

FIG. 1 illustrates a block diagram of an exemplary system for automatedassociation of product information with electronic shelf labels inaccordance with some embodiments described herein.

FIG. 2A illustrates an overhead view of an exemplary embodiment thatobtains images of paper shelf labels on modular units.

FIG. 2B illustrates an overhead view of the exemplary embodiment of FIG.2A obtaining images of electronic shelf labels on modular units.

FIG. 3A illustrates an image of paper labels obtained in an exemplaryembodiment.

FIG. 3B illustrates an image of electronic shelf labels obtained in anexemplary embodiment.

FIG. 4 illustrates a block diagram of a remote computing device suitablefor use with exemplary embodiments.

FIG. 5 illustrates a network environment suitable for use with exemplaryembodiments.

FIG. 6 illustrates a flowchart for a method for automated association ofproduct information with electronic shelf labels in an exemplaryembodiment.

DETAILED DESCRIPTION

Described in detail herein are systems and methods for automatedassociation of product information with electronic shelf labels. Thesystems and methods employ an autonomous robotic vehicle (ARV) alone orin combination with a remote computing device to detect pre-existingproduct information in the form of paper labels located on modularunits. The ARV can then detect the location of electronic shelf labels(ESLs) after installation and can associate the pre-existing productinformation gleaned from the paper labels with the corresponding ESLs.

ESLs are an increasingly desirable way to display product information topurchasers at a retailer. Because information displayed on ESLs can beautomatically updated from a central control server, or at least updatedwirelessly from a local device, pricing or other information can beupdated or corrected on a regular basis without requiring an entity(such as a retail employee) to physically walk to the shelf and replacethe paper label with a new label containing updated information.

When a retailer opts to change from the existing paper labels to ESLs,the modular units that display products are conventionally modified toaccommodate the ESLs. This is often done without removing the productsfrom the modular unit. The process can involve removal of a portion ofthe modular unit that retains the paper labels, such as but not limitedto shelf facings, and installation of a new portion that includeselectronic shelf labels. Conventionally, after installation of the newportion including the ESLs, a person manually identifies each ESLone-by-one, consults the corresponding paper label (since removed fromthe modular unit) to determine the product information that should beassociated with the ESL, and individually programs the ESL with theappropriate product information. This manual process utilizes asignificant amount of labor to singly program each of the thousands ofESLs in a given retail facility. In addition, the manual process isrepetitive and, thus, highly error-prone as it can be difficult tomaintain correspondence between the ESLs and the removed paper labelsover a work shift when each association must be made individually.Furthermore, errors are particularly difficult to detect for certainESLs that display only price information for a product as the displayedprice may not immediately indicate to the viewer that the association ofthe ESL with a product was made incorrectly.

Systems and methods are described herein to automate the process ofconversion for a facility from paper shelf labels to electronic shelflabels. By using an autonomous robotic vehicle to obtain initial imagesof paper shelf labels before removal and subsequent images of electronicshelf labels after placement on the shelf, systems and methods describedherein can re-program many multiple ESLs in a batch-processing fashion.As a result, the time and cost associated with initial manualprogramming of the ESLs and costs associated with correcting errors inthe programming process are significantly reduced. Moreover, the processcan be performed without human intervention, which enables theprogramming to be performed by the autonomous robotic vehicle and/orremote computing device while human labor resources are allocatedelsewhere. Additionally, in some embodiments, the ARV can determinecompliance or non-compliance of a modular unit with a planogram for thefacility.

FIG. 1 illustrates a system 100 for automated association of productinformation with electronic shelf labels in accordance with an exemplaryembodiment. The system 100 includes an autonomous robotic vehicle (ARV)110 and a remote computing device 150. The ARV 110 includes a memory116, a processor 115, at least one sensor 112, and a communicationsinterface 114. The sensor 112, may be, but is not limited to, a cameraor video camera capable of obtaining still or moving images. Optionally,the memory 116 of the ARV 110 can store an identification module 160that can be executed by the processor 115. In one embodiment, the ARV isa ground-based autonomous vehicle. In another embodiment, the ARV may bean Unmanned Aerial Vehicle (UAV) capable of flight. The remote computingdevice 150 includes a processor 155, a communications interface 154, anda memory 156 that may store the identification module 160 that can beexecuted by the processor 155. The remote computing device 150 and/orthe ARV 110 can be in communication with one or more databases 152 thatinclude product information 142 related to products stored on themodular units. In some embodiments, the database 152 including productinformation 142 is implemented within the remote computing device 150.In some embodiments, the remote computing device 150 and/or the ARV 110can be in communication with one or more ESLs 134 disposed on a modularunit.

Continuing with the description of FIG. 1, the ARV 110 may executeinstructions causing it to obtain one or more initial images of one ormore modular units in a facility in which paper shelf labels on themodular units appear. The ARV 110 is configured to transmit the initialimages to the remote computing device 150 using the communicationsinterface 114. The ARV 110 may also execute instructions causing it toobtain one or more subsequent images of the same modular units in whichelectronic shelf labels 134 appear. The ARV 110 is configured totransmit the subsequent images to the remote computing device 150 usingthe communications interface 114. The remote computing device 150receives the initial and subsequent images via the communicationsinterface 154. The remote computing device 150 also executes theidentification module 160 to determine the product information 142associated with paper shelf labels 132 in the initial images and todetermine identifying information for the electronic shelf labels 134 inthe subsequent images. The execution of the identification module 160determines the correspondence between the paper shelf labels 132 in theinitial images and the ESLs 134 in the subsequent images and associatesthe proper product information 142 with the ESLs 134. Once informed ofthe association, the ARV 110 or remote computing device 150 can programthe ESL 134 to display the correct product information 142. Byautomating identification and association between paper shelf labels andESLs, the system 100 reduces human involvement in the process ofpreparing and programming the replacement ESLs upon removal of papershelf labels on modular units and reduces rates of error in programmingof the ESLs.

As shown in FIGS. 2A and 2B, the ARV 110 can move in relation to themodular units 130 in the facility. In some embodiments, the ARV 110 caninclude wheels or treads to enable motion laterally with respect to themodular units 130 or to enable motion closer to or further from themodular units 130. In other embodiments, the ARV may hover in proximityof modular units containing paper labels or ESLs in a position enablingthe ARV to obtain images. As the ARV 110 moves in relation to themodular units 130, the sensor 112 can obtain initial images of themodular units 130 and associated paper labels 132 as shown schematicallyin FIG. 2A. Each of the paper labels 132 can correspond to a productstored on the modular unit 130. In some embodiments, the images are sentfrom the ARV 110 to the remote computing device 150. For example, theARV 110 may communicate with remote computing device 150 viacommunications interface 114 of the ARV 110 and communications interface154 of the remote computing device 150. In some embodiments, thecommunication may be performed using a wired or wireless communicationstandard including, but not limited to, 802.11x, BlueTooth®, Wi-Max, orany other suitable communications standard. As described below ingreater detail, the initial images can be retained for further analysisat the ARV 110 in embodiments without a remote computing device 150.Movement of the ARV 110 and acquisition of images can be controlled bythe processor 114 executing instructions on-board the ARV 110 in someembodiments.

After the image acquisition described above in relation to FIG. 2A, themodular units 130 can be prepared for conversion to electronic shelflabels. For example, the modular units 130 can include a removableedge/shelf facing portion including the labels at the front of eachshelf. The original removable edge portion including paper labels 132can be removed and replaced with a new removable edge portion includingESLs 134. In some embodiments, the new removable edge portion caninclude a same number of ESLs 134 as the number of paper labels 132 onthe original removable edge portion. In addition, each ESL 134 can be ina same position with respect to the removable edge portion as a positionof the corresponding paper label 132 on the original removable edgeportion.

After installation of the ESLs 134 on the modular units 130, the ARV 110can move relative to the modular units 130 and acquire subsequent imagesof the modular units 130 (subsequent to the addition of the ESLs) andassociated ESLs 134 as shown schematically in FIG. 2B.

FIG. 3A depicts a portion of an image 300 obtained by the ARV 110 duringthe image acquisition process depicted in FIG. 2A. In the image 300, themodular unit 130, paper labels 132, and products 140 situated on shelves135 of the modular unit 130 can appear. In some embodiments, a modularunit identifier 138 associated with the modular unit 130 can appear inthe image 300. Although only a single image 300 is illustrated herein,it should be appreciated that the ARV 110 may obtain multiple images ofthe modular units 130 as the ARV 110 moves relative to the modular units130 in exemplary embodiments. In some embodiments, the multiple imagescan include overlapping image content to enable stitching of theseparate images or a similar method to identify the same objects inseparate images.

The image 300 can be analyzed by the identification module 160performing video analytics to identify the paper shelf labels 132appearing in the image 300. In some embodiments, the sensor 112 of theARV 110 can acquire images of sufficiently high resolution thatsubsequent analysis can resolve information appearing on the paper shelflabels 132 from several feet away. For example, the sensor 112 caninclude optics and/or detection elements (such as charge coupled devicesor CCDs) capable of producing an image including legible paper shelflabels 132 with 8-10 point font from five feet away. In someembodiments, the paper shelf labels 132 can include informationassociated with one or more products 140. For example, the paper shelflabels 132 can include a Universal Product Code (UPC), price informationfor the product, product serial numbers or other identification numbers,or a two-dimensional machine-translatable code such as a barcode or a QRCode® that identifies the product.

In some embodiments, the identification module 160 is stored in thememory 156 of the remote computing device 150, and the initial images300 are transmitted from the ARV 110 to the remote computing device 150for analysis. In some embodiments, the identification module 160 isstored in the memory 116 of the ARV 110, and the initial image 300 isanalyzed locally in the ARV 110.

In some embodiments, the memory 116 of the ARV 110 or the memory 156 ofthe remote computing device 150 can include one or more label templates.The one or more label templates can include information, for example, asto the location of a barcode or other information within the borders ofthe paper label 132. As part of the image analysis and informationextraction performed by the identification module 160, portions of theinitial image 300 including images of paper shelf labels 132 can becompared to the one or more label templates to improve accuracy inisolation and/or determination of information appearing on the papershelf labels 132.

In some embodiments, the identification module 160 can compareinformation obtained from the paper shelf labels 132 to productinformation 142 retrieved from the one or more databases 152. Thecomparison ensures that the information was obtained without error fromthe product shelf labels 132. Additionally, the comparison enables theidentification module 160 to determine which product information 142stored in the one or more databases is associated with each of the papershelf labels 132.

The identification module 160 can assess the location of the paper shelflabels 132 with respect to the modular units 130, with respect to one ormore products 140 on the shelves 135, or with respect to both. Theidentification module 160 can identify the paper shelf labels 132 andassociate the paper shelf label 132 with the nearest product 140 in someembodiments. In some embodiments, the identification module 160 canassociate a location of each paper shelf label 132 on the modular unit130 with the corresponding product information 142 in the database.

After the ARV 110 acquires initial images (of which image 300 is anexample), the paper shelf labels 132 are removed from the modular units130. Then, ESLs 134 are affixed to the modular units 130 and subsequentimages are acquired as described next.

FIG. 3B illustrates a portion of an example image 300′ obtained by theARV 110 during the image acquisition process depicted in FIG. 2B afterESLs 134 have been affixed to the modular units 130. In the image 300′,the modular units 130, ESLs 134, and products 140 situated on shelves135 of the modular unit 130 can appear. In some embodiments, the modularunit identifier 138 associated with the modular unit 130 can appear inthe image 300′. In some embodiments, the ESLs 134 can includeidentifying information. For example, the paper shelf labels 132 caninclude a serial number or other individualized number or atwo-dimensional machine-translatable code such as a barcode or a QRCode® that identifies the ESL 134. As described above with respect toFIG. 3A, the sensor 112 can produce images 300′ of sufficient quality asto enable the resolution and/or analysis of identifying informationdisplayed on the ESL 134.

In some embodiments, the identification module 160 is stored in thememory 156 of the remote computing device 150, and the subsequent image300′ is transmitted from the ARV 110 to the remote computing device 150for analysis. In some embodiments, the identification module 160 isstored in the memory 116 of the ARV 110, and the subsequent image 300′is analyzed locally in the ARV 110.

The identification module 160 can assess the location of the ESLs 134with respect to the modular units 130, with respect to one or moreproducts 140 on the shelves 135, or with respect to both. Theidentification module 160 can identify the ESLs 134 and associate theESLs 134 with the nearest product 140 in some embodiments.

The identification module 160 identifies a correspondence between eachof the ESLs 134 in the subsequent image 300′ and one of the paper shelflabels 132 in the initial image 300. The correspondence can beidentified based upon the locations of the paper shelf label 132 and theESL 134 relative to the modular unit 130, relative to products 140 onshelves 135, or both. When a paper shelf label 132 is identified asbeing at a particular location in image 300 and an ESL 134 is identifiedas being at the same location in image 300′, the paper shelf label 132and the ESL 134 correspond.

The identification module 160 associates product information 142previously assigned to each of the paper shelf labels 132 to thecorresponding ESL 134. In this way, each ESL 134 affixed on the modularunit 130 is properly associated with the product nearest to it on theshelf 135. In some embodiments, the identification module 160 cantransmit instructions to the ARV 110 to program the ESL 134 with theassociated product information 142. Alternatively, if the remotecomputing device 150 is able to communicate directly or indirectly withthe ESL, the remote computing device can program each ESL 134 withproduct information 142 by transmitting instructions to do so via thecommunications interface 154. In some embodiments, the ESL 134 candisplay the product information 142 such as, but not limited to, priceinformation.

In some embodiments, the identification module 160 performs videoanalytics and identifies and analyzes the modular unit identifier 138disposed on the modular unit 130 and appearing in the initial images300, the subsequent images 300′, or both. The modular unit identifier138 can include information specific to each modular unit 130 such as aserial number or two-dimensional machine-translatable code. In someembodiments, the modular unit identifier 138 can include informationrelated to the position of the modular unit 130 within the facility suchas a number or graphic keyed to a planogram of the facility. Theidentification module 160 can identify a location of the modular unit130 within the facility based on the analysis of the modular unitidentifier 138 with respect to stored facility location information. Insome embodiments, the analysis of the modular unit identifier 138includes an analysis of the planogram of the facility. Once the locationof the modular unit 130 within the facility has been identified, thelocation can be associated with the identifying information of acorresponding ESL 134 that is affixed to that modular unit 130.Identification of the location of an ESL 134 (on a modular unit 130)within the facility provides the advantage that the ESL 134 can beprogrammed with product information 142 that is tailored to the locationof the associated product within the facility. For example, the facilitymay have two customer zones in which a product is sold at differentprices. The first zone may be the general merchandise section of thefacility while the second zone may be a special “convenience” section, alimited-availability sale section (e.g., a section including“doorbuster” products in limited quantities or for limited times), or aspecialized section such as a home and garden section. Thus, an ESL 134for the same product may display different product information 142depending upon the location of the ESL 134 within the facility. Theidentification module 160 can program the ESL 134 with productinformation 142 that takes into account not only the identifyinginformation of the ESL 134 but also associated location information.

In some embodiments, the ARV 110 stores a planogram of the facility inmemory and can check the accuracy of the planogram of the facility afterimage acquisition. For example, the ARV 110 can confirm that one or moreESLs 134 (e.g., the location or identity of the ESLs 134) corresponds tothe planogram of the facility and transmits a notification to the remotecomputing device 150. Alternatively or in addition, the ARV 110 canconfirm that one or more ESLs 134 fail to correspond to the planogram ofthe facility and can transmit a notification to the remote computingdevice 150. The notification can include the identifying information forthe one or more ESLs 134. Upon receipt of the notification that the ESLfails to correspond to the planogram, the remote computing device 150can issue an alert. In one embodiment, the alert may be transmitted to astore associate that can then remedy the discrepancy if necessary. Inanother embodiment, the alert may be transmitted to the same ordifferent ARV capable of performing an action to remedy the planogramissue. For example, if the ARV is equipped with an articulating armcapable of placing and removing items, the ARV may be tasked by theremote computing device with adding or removing items to or from themodular unit until the modular unit corresponds with the planogram.

FIG. 4 is a block diagram of a remote computing device 150 suitable foruse with exemplary embodiments of the present disclosure. The remotecomputing device 150 may be, but is not limited to, a smartphone,laptop, tablet, desktop computer, server, or network appliance. Theremote computing device 150 includes one or more non-transitorycomputer-readable media for storing one or more computer-executableinstructions or software for implementing exemplary embodiments. Thenon-transitory computer-readable media may include, but are not limitedto, one or more types of hardware memory, non-transitory tangible media(for example, one or more magnetic storage disks, one or more opticaldisks, one or more flash drives, one or more solid state disks), and thelike. For example, memory 156 included in the remote computing device150 may store computer-readable and computer-executable instructions orsoftware (e.g., identification module 160 for implementing exemplaryoperations of the remote computing device 150 such as identificationmodule 160. The remote computing device 150 also includes configurableand/or programmable processor 155 and associated core(s) 404, andoptionally, one or more additional configurable and/or programmableprocessor(s) 402′ and associated core(s) 404′ (for example, in the caseof computer systems having multiple processors/cores), for executingcomputer-readable and computer-executable instructions or softwarestored in the memory 156 and other programs for implementing exemplaryembodiments of the present disclosure. Processor 155 and processor(s)402′ may each be a single core processor or multiple core (404 and 404′)processor. Either or both of processor 155 and processor(s) 402′ may beconfigured to execute one or more of the instructions described inconnection with remote computing device 150.

Virtualization may be employed in the remote computing device 150 sothat infrastructure and resources in the remote computing device 150 maybe shared dynamically. A virtual machine 412 may be provided to handle aprocess running on multiple processors so that the process appears to beusing only one computing resource rather than multiple computingresources. Multiple virtual machines may also be used with oneprocessor.

Memory 156 may include a computer system memory or random access memory,such as DRAM, SRAM, EDO RAM, and the like. Memory 156 may include othertypes of memory as well, or combinations thereof.

A user may interact with the remote computing device 150 through avisual display device 152, such as a computer monitor, which may displayone or more graphical user interfaces 416. The user may interact withthe remote computing device 150 using a multi-point touch interface 420or a pointing device 418.

The remote computing device 150 may also include one or more computerstorage devices 426, such as a hard-drive, CD-ROM, or other computerreadable media, for storing data and computer-readable instructionsand/or software that implement exemplary embodiments of the presentdisclosure (e.g., applications). For example, exemplary storage device426 can include one or more databases 152 for storing productinformation 142, location information for paper shelf labels 132 or ESLs134, planograms of the facility, or identifying information related toESLs 134. The databases 152 may be updated manually or automatically atany suitable time to add, delete, and/or update one or more data itemsin the databases.

The remote computing device 150 can include a communications interface154 configured to interface via one or more network devices 424 with oneor more networks, for example, Local Area Network (LAN), Wide AreaNetwork (WAN) or the Internet through a variety of connectionsincluding, but not limited to, standard telephone lines, LAN or WANlinks (for example, 802.11, T1, T3, 56 kb, X.25), broadband connections(for example, ISDN, Frame Relay, ATM), wireless connections, controllerarea network (CAN), or some combination of any or all of the above. Inexemplary embodiments, the remote computing device 150 can include oneor more antennas 422 to facilitate wireless communication (e.g., via thenetwork interface) between the remote computing device 150 and a networkand/or between the remote computing device 150 and the ARV 110. Thecommunications interface 154 may include a built-in network adapter,network interface card, PCMCIA network card, card bus network adapter,wireless network adapter, USB network adapter, modem or any other devicesuitable for interfacing the remote computing device 150 to any type ofnetwork capable of communication and performing the operations describedherein.

The remote computing device 150 may run operating system 410, such asversions of the Microsoft® Windows® operating systems, differentreleases of the Unix and Linux operating systems, versions of the MacOS®for Macintosh computers, embedded operating systems, real-time operatingsystems, open source operating systems, proprietary operating systems,or other operating system capable of running on the remote computingdevice 150 and performing the operations described herein. In exemplaryembodiments, the operating system 410 may be run in native mode oremulated mode. In an exemplary embodiment, the operating system 410 maybe run on one or more cloud machine instances.

FIG. 5 illustrates a network environment 500 including the ARV 110 andremote computing system 150 suitable for use with exemplary embodiments.The network environment 500 can include one or more databases 152, oneor more ARVs 110, one or more ESLs 134, and one or more remote computingdevices 150 that can communicate with one another via a communicationsnetwork 505.

The remote computing device 150 can host one or more applications (e.g.,the identification module 160) configured to interact with one or morecomponents of the ARVs 110 and/or to facilitate access to the content ofthe databases 152. The databases 152 may store information or data asdescribed above herein. For example, the databases 152 can includeproduct information 142, identifying information for one or more ESLs134, one or more planograms for the facility, and location informationassociated with paper shelf labels 132 and/or ESLs 134. The databases152 can be located at one or more geographically distributed locationsaway from the ARVs 110 and/or the remote computing device 150.Alternatively, the databases 152 can be located at the same geographicallocation as the remote computing device 150 and/or at the samegeographical location as the ARVs 110.

In an example embodiment, one or more portions of the communicationsnetwork 505 can be an ad hoc network, a mesh network, an intranet, anextranet, a virtual private network (VPN), a local area network (LAN), awireless LAN (WLAN), a wide area network (WAN), a wireless wide areanetwork (WWAN), a metropolitan area network (MAN), a portion of theInternet, a portion of the Public Switched Telephone Network (PSTN), acellular telephone network, a wireless network, a Wi-Fi network, a WiMAXnetwork, an Internet-of-Things (IoT) network established usingBlueTooth® or any other protocol, any other type of network, or acombination of two or more such networks.

FIG. 6 illustrates a flowchart for a method 600 for automatedassociation of product information with electronic shelf labels in anexemplary embodiment. The method 600 includes obtaining initial images300 of modular units 130 in a facility using at least one sensor 112 ofan autonomous robot vehicle (ARV) 110 (step 602). The modular units 130include multiple paper shelf labels 132. The initial images 300 aretaken before removal of the paper shelf labels 132 from the modularunits 130. The method 600 further includes obtaining, using the at leastone sensor 112, subsequent images 300′ of the modular units 130 (step604). The subsequent images 300′ are taken after multiple electronicshelf labels 134 are affixed to the modular units 130.

The method 600 also includes retrieving product information 142 from oneor more databases 152 holding product information 142 associated withproducts 140 assigned to the modular units 130 in the facility (step606). The method 600 additionally includes analyzing the initial images300 to identify the paper shelf labels 132 appearing in the initialimages 300 to determine the product information 142 associated with eachof the paper shelf labels 132 (step 608). The method 600 includesanalyzing the electronic shelf labels 134 disposed on the modular units130 that appear in the subsequent images 300′ to determine identifyinginformation associated with each of the electronic shelf labels 134(step 610).

Additionally, the method 600 includes identifying a correspondencebetween each of the electronic shelf labels 134 and one of the papershelf labels 132 (step 612). The method 600 also includes associatingproduct information 142 previously assigned to each of the paper shelflabels 133 with the corresponding one of the electronic shelf labels 134(step 614). Following the association of paper shelf label to ESL, thecorresponding one of the electronic shelf labels is programmed with theproduct information by the remote computing device or the ARV (step616).

In describing exemplary embodiments, specific terminology is used forthe sake of clarity. For purposes of description, each specific term isintended to at least include all technical and functional equivalentsthat operate in a similar manner to accomplish a similar purpose.Additionally, in some instances where a particular exemplary embodimentincludes multiple system elements, device components or method steps,those elements, components or steps may be replaced with a singleelement, component, or step. Likewise, a single element, component, orstep may be replaced with multiple elements, components, or steps thatserve the same purpose. Moreover, while exemplary embodiments have beenshown and described with references to particular embodiments thereof,those of ordinary skill in the art will understand that varioussubstitutions and alterations in form and detail may be made thereinwithout departing from the scope of the present disclosure. Furtherstill, other aspects, functions, and advantages are also within thescope of the present disclosure.

Exemplary flowcharts are provided herein for illustrative purposes andare non-limiting examples of methods. One of ordinary skill in the artwill recognize that exemplary methods may include more or fewer stepsthan those illustrated in the exemplary flowcharts, and that the stepsin the exemplary flowcharts may be performed in a different order thanthe order shown in the illustrative flowcharts.

1. A system for automated association of product information withelectronic shelf labels, comprising: a remote computing device thatincludes a processor, a memory, and a communications interface, theremote computing device configured to execute an identification module;one or more databases holding product information associated withproducts assigned to a plurality of modular units in a facility; and alocal computing device located in the facility and that includes atleast one sensor, a communications interface, a processor, and a memory;wherein the identification module when executed is configured to:receive, from the local computing device, a plurality of initial imagesof a plurality of modular units in the facility, the plurality ofmodular units including a plurality of paper shelf labels, the pluralityof initial images taken before a removal of the plurality of paper shelflabels from the plurality of modular units; receive, from the localcomputing device, a plurality of subsequent images of the plurality ofmodular units, the plurality of subsequent images taken after aplurality of electronic shelf labels are affixed to the plurality ofmodular units; retrieve product information from the one or moredatabases; analyze the plurality of initial images to identify theplurality of paper shelf labels appearing in the plurality of initialimages to determine the product information associated with each of theplurality of paper shelf labels; analyze the plurality of electronicshelf labels disposed on the modular unit that appear in the pluralityof subsequent images to determine identifying information associatedwith each of the plurality of electronic shelf labels; identify acorrespondence between each of the plurality of electronic shelf labelsand one of the plurality of paper shelf labels; and associate productinformation previously assigned to each of the plurality of paper shelflabels with the corresponding one of the plurality of electronic shelflabels, wherein the corresponding one of the plurality of electronicshelf labels is programmed with the product information.
 2. The systemof claim 1, wherein the identification module when executed: transmitsinstructions to the local computing device to program the plurality ofelectronic shelf labels with the associated product information.
 3. Thesystem of claim 1, wherein the identification module when executed:analyzes a modular unit identifier disposed on the modular unit andappearing in the plurality of initial images; identifies a location ofthe modular unit within the facility based on the analysis of themodular unit identifier and a planogram of the facility; and associatesthe location with the identifying information of a corresponding one ofthe plurality of electronic shelf labels.
 4. The system of claim 1,wherein the local computing device stores a planogram of the facilityand uses the planogram to confirm that one of the plurality ofelectronic shelf labels corresponds to a planogram of the facility andtransmits a notification to the remote computing device.
 5. The systemof claim 1, wherein the local computing device stores a planogram of thefacility and uses the planogram to confirm that one of the plurality ofelectronic shelf labels fails to correspond to a planogram of thefacility and transmits a notification to the remote computing device. 6.A system for automated association of product information withelectronic shelf labels, comprising: one or more databases holdingproduct information associated with products assigned to a plurality ofmodular units in a facility; and a local computing device in thefacility and that includes at least one sensor, an identificationmodule, a processor, and a memory; wherein the identification modulewhen executed is configured to: receive, from the local computingdevice, a plurality of initial images of a plurality of modular units inthe facility, the plurality of modular units including a plurality ofpaper shelf labels, the plurality of initial images taken before aremoval of the plurality of paper shelf labels from the plurality ofmodular units; receive, from the local computing device, a plurality ofsubsequent images of the plurality of modular units, the plurality ofsubsequent images taken after a plurality of electronic shelf labels areaffixed to the plurality of modular units; retrieve product informationfrom the one or more databases; analyze the plurality of initial imagesto identify the plurality of paper shelf labels appearing in theplurality of initial images to determine the product informationassociated with each of the plurality of paper shelf labels; analyze theplurality of electronic shelf labels disposed on the modular unit thatappear in the plurality of subsequent images to determine identifyinginformation associated with each of the plurality of electronic shelflabels; identify a correspondence between each of the plurality ofelectronic shelf labels and one of the plurality of paper shelf labels;and associate product information previously assigned to each of theplurality of paper shelf labels with the corresponding one of theplurality of electronic shelf labels, wherein the corresponding one ofthe plurality of electronic shelf labels is programmed with the productinformation.
 7. The system of claim 6, wherein the local computingdevice includes a communications interface and the identification modulewhen executed: programs the plurality of electronic shelf labels withthe associated product information using the communications interface.8. The system of claim 6, wherein the identification module whenexecuted: analyzes a modular unit identifier disposed on the modularunit and appearing in the plurality of initial images; identifies alocation of the modular unit within the facility based on the analysisof the modular unit identifier and a planogram of the facility; andassociates the location with the identifying information of acorresponding one of the plurality of electronic shelf labels.
 9. Thesystem of claim 6, wherein the local computing device stores a planogramof the facility and uses the planogram to confirm that one of theplurality of electronic shelf labels corresponds to a planogram of thefacility and transmits a notification to a remote computing device. 10.The system of claim 6, wherein the local computing device stores aplanogram of the facility and uses the planogram to confirm that one ofthe plurality of electronic shelf labels fails to correspond to aplanogram of the facility and transmits a notification to a remotecomputing device.
 11. The system of claim 6 wherein the local computingdevice includes the one or more databases.
 12. The system of claim 6wherein the one or more databases include one or more communicationsinterfaces and the local computing device includes a communicationsinterface, and wherein the identification module retrieves productinformation from the one or more databases by using the communicationsinterface of the local computing device to receive product informationfrom the one or more communications interfaces of the one or moredatabases.
 13. A method for automated association of product informationwith electronic shelf labels, comprising: receiving, from a localcomputing device located in a facility, a plurality of initial images ofa plurality of modular units in the facility, the plurality of modularunits including a plurality of paper shelf labels, the plurality ofinitial images taken before a removal of the plurality of paper shelflabels from the plurality of modular units; receiving, from the localcomputing device, a plurality of subsequent images of the plurality ofmodular units, the plurality of subsequent images taken after aplurality of electronic shelf labels are affixed to the plurality ofmodular units; retrieving product information from one or more databasesholding product information associated with products assigned to theplurality of modular units in the facility, analyzing the plurality ofinitial images to identify the plurality of paper shelf labels appearingin the plurality of initial images to determine the product informationassociated with each of the plurality of paper shelf labels, analyzingthe plurality of electronic shelf labels disposed on the plurality ofmodular units that appear in the plurality of subsequent images todetermine identifying information associated with each of the pluralityof electronic shelf labels, identifying a correspondence between each ofthe plurality of electronic shelf labels and one of the plurality ofpaper shelf labels, and associating product information previouslyassigned to each of the plurality of paper shelf labels with thecorresponding one of the plurality of electronic shelf labels; andprogramming the corresponding one of the plurality of electronic shelflabels with the product information.
 14. The method of claim 13, furthercomprising: programming the plurality of electronic shelf labels withthe associated product information using the local computing device. 15.The method of claim 13, further comprising: analyzing a modular unitidentifier disposed on a modular unit in the plurality of modular unitsand appearing in the plurality of initial images; identifying a locationof the modular unit within the facility based on the analysis of themodular unit identifier and a planogram of the facility; and associatingthe location with the identifying information of a corresponding one ofthe plurality of electronic shelf labels.
 16. The method of claim 13,wherein the local computing device stores a planogram of the facilityand further comprising: determining using the stored planogram whetherone of the plurality of electronic shelf labels corresponds to aplanogram of the facility; and upon a determination of correspondence,transmitting a notification from the local computing device to a remotecomputing device.
 17. The method of claim 13, wherein the localcomputing device stores a planogram of the facility and furthercomprising: determining using the stored planogram whether one of theplurality of electronic shelf labels fails to correspond to a planogramof the facility; and upon a determination of failure to correspond,transmitting a notification from the local computing device to a remotecomputing device.
 18. The method of claim 13, further comprising:transmitting the plurality of subsequent images to the remote computingdevice; and transmitting the plurality of initial images to a remotecomputing device using a communications interface of the local computingdevice, and wherein analyzing the plurality of initial images andanalyzing the plurality of electronic shelf labels disposed on themodular units that appear in the plurality of subsequent images isperformed by the remote computing device.
 19. The system of claim 1,wherein the local computing device is an autonomous robotic vehicle orforms a part thereof.